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返回到 Probabilistic Graphical Models 2: Inference

学生对 Stanford University 提供的 Probabilistic Graphical Models 2: Inference 的评价和反馈

4.6
488 个评分

课程概述

Probabilistic graphical models (PGMs) are a rich framework for encoding probability distributions over complex domains: joint (multivariate) distributions over large numbers of random variables that interact with each other. These representations sit at the intersection of statistics and computer science, relying on concepts from probability theory, graph algorithms, machine learning, and more. They are the basis for the state-of-the-art methods in a wide variety of applications, such as medical diagnosis, image understanding, speech recognition, natural language processing, and many, many more. They are also a foundational tool in formulating many machine learning problems. This course is the second in a sequence of three. Following the first course, which focused on representation, this course addresses the question of probabilistic inference: how a PGM can be used to answer questions. Even though a PGM generally describes a very high dimensional distribution, its structure is designed so as to allow questions to be answered efficiently. The course presents both exact and approximate algorithms for different types of inference tasks, and discusses where each could best be applied. The (highly recommended) honors track contains two hands-on programming assignments, in which key routines of the most commonly used exact and approximate algorithms are implemented and applied to a real-world problem....

热门审阅

AA

Mar 8, 2020

Great course, except that the programming assignments are in Matlab rather than Python

YP

May 28, 2017

I learned pretty much from this course. It answered my quandaries from the representation course, and as well deepened my understanding of PGM.

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26 - Probabilistic Graphical Models 2: Inference 的 50 个评论(共 78 个)

创建者 Julio C A D L

Apr 9, 2018

I would have like to complete the honors assignments, unfortunately, I'm not fluent in Matlab. Otherwise, great course!

创建者 kat i

Dec 6, 2020

Amazing course offering a technical as well as intuitional understanding of the principles of doing inference

创建者 Evgeniy Z

Mar 10, 2018

Very interesting course. However, even after completing it with honors, I feel like I don't understand a lot.

创建者 HARDIAN L

May 19, 2020

Great balance between theories and practices. Also provide a lot of intuitions to understand the concepts

创建者 Una S

Sep 2, 2020

Amazing course! Loved how Daphne explained very complicated things in an understandable manner!

创建者 Martin P

Jan 19, 2021

Great course! Course has filled gaps in my knowledge from statistics and similar sciences.

创建者 Ruiliang L

Feb 24, 2021

Awesome class to gain better understanding of inference for graphical model

创建者 Sriram P

Jun 24, 2017

Had a wonderful and enriching fun filled experience, Thank you Daphne Ma'am

创建者 Jerry R

Dec 22, 2017

Great course! Expect to spend significant time reviewing the material.

创建者 vineetha m

Dec 4, 2024

I have found this course very useful for my research work in robotics

创建者 Anil K

Nov 5, 2017

This course induces lateral thinking and deep reasoning.

创建者 Liu Y

Mar 18, 2018

Really a interesting, challenging and great course!

创建者 KE Z

Dec 29, 2017

Very valuable course! I am glad I made it.

创建者 Tim R

Oct 4, 2017

Very interesting, more advanced material

创建者 Arthur C

Jul 19, 2017

Difficult, but it makes you think a lot!

创建者 Damir H

Jul 31, 2023

Very interesting and exciting course.

创建者 Dat Q D

Jan 25, 2022

the content is very hard

创建者 chen h

Feb 5, 2018

Interest but difficult.

创建者 Ramasubramanian G

Sep 14, 2017

Great job Prof. Koller!

创建者 Musalula S

Aug 2, 2018

This is a great course

创建者 Wei C

Mar 6, 2018

good way to learn PGM,

创建者 Alexander K

Jun 3, 2017

Thank You for all.

创建者 Wenjun W

May 21, 2017

Awesome class!

创建者 郭玮

Nov 12, 2019

Very helpful.

创建者 Anderson R L

Nov 3, 2017

Great course!